Solutions Architect Interview Questions

"I spent about six years as a backend engineer, mostly on payment and order systems. Over time I noticed I was the person pulled into meetings whenever a change touched three or four teams, because I could explain how the pieces fitted together and what would break. In my last role I led the design for moving our checkout off an old monolith, which meant writing the target architecture, running reviews with security and operations, and explaining the cost to finance. That's when I realised I enjoyed the translation work more than writing the code myself. I want a solutions architect role now because I'd like to do that full time, across more customers and problems, while staying close enough to delivery that my designs stay honest."

"The way I see it, they're three zoom levels. The enterprise architect looks across the whole organisation: which platforms we standardise on, how capabilities map to systems, and the roadmap over several years. The solutions architect takes one business problem, say a new customer onboarding journey, and designs a solution that meets its requirements, fits those standards, and can actually be delivered by the teams and budget we have. The senior engineer owns how a component is built: code structure, testing, and the performance of that piece. In practice the lines blur. I'll review code-level choices if they affect the architecture, and I'll push back to enterprise architecture when a standard doesn't fit the problem. What matters is being clear who owns which decision."

"Most of my work has been in retail and logistics: order management, warehouse integrations, customer portals, and a fair amount of connecting modern services to older ERP systems. Before this interview I read up on your market, and I think a lot transfers. You also have high transaction volumes, lots of partner integrations and peaks you can predict. What's new to me is the regulatory side. I haven't designed under your audit and data retention rules, so in my first weeks I'd sit with your compliance and security people, read the controls that apply to our designs, and look at two or three recent architecture reviews to see what they actually catch. I'd rather ask early than find out in an audit."

"I protect some hands-on time every week. When I'm proposing something new, I build a small proof of concept myself, even if it gets thrown away, because it shows me the awkward parts the documentation doesn't mention. I read pull requests on services I've designed, not to police them, but to see where the design is fighting the code. I read incident reports and join post-mortems, because production tells you which of your assumptions were wrong. For new technology, I follow a few engineering blogs and the release notes of the platforms we use, and I try anything I'm tempted to recommend in a sandbox first. And I ask engineers directly what's painful. If I haven't been surprised by something in a while, that usually means I'm too far from the work."

"First I'd find out where the days actually go today. Usually it isn't one slow system. It's a quote waiting for someone to check stock, a pricing approval, and data typed from one tool into another. So I'd walk the current process with the people who do it. Then I'd pin down numbers: how many quotes a day, what 'minutes' really means, whether every quote qualifies or only standard ones, and how accurate a quote must be. Then constraints: which systems own prices and stock, which approvals are genuinely required, what can't change. From that I'd write functional requirements, like automatic pricing for standard products, and non-functional ones, like a quote back in under two minutes for most requests. Finally I'd play it back to the sales director in plain words, because if we disagree on the goal, the best design won't help."

"The ones I always pin down are availability, response time, expected load and growth, recovery targets, security and compliance needs, and how the system will be run and supported. For each, I push for a statement we can test. 'Fast' becomes something like: search results in under a second for most requests at twice today's peak load. 'Always up' becomes a monthly availability target, plus a recovery point and recovery time objective, meaning how much data we can afford to lose and how long we can be down. Then I show the price of each level. Running actively in two regions costs a lot more in money and complexity than one region with good backups, so I'll ask what an hour of downtime really costs them. Once the business chooses, those numbers go into the design and into our load and failover tests."

"At my last company, operations asked for a real-time dashboard showing every warehouse order, updating second by second. It sounded like a big streaming project. When I sat with the shift leads, I asked what they'd do differently when they saw a number change. It turned out they only acted on one thing: orders at risk of missing the courier cut-off. They didn't need everything live, they needed to know early about the few that were running late. So instead of a streaming platform, we built a job that checked open orders every few minutes against cut-off times and alerted the right shift lead, plus a simple list view. It shipped in a few weeks rather than months, and late shipments dropped noticeably. The operations head was happy because I'd framed it around his goal, not around saying no."

"Customers rarely know requests per second, but they know their business. So I'd ask things they can answer: how many customers, how many orders on a busy day, when the peaks are, and what happens to them if the system is down for an hour on a Monday morning. From that I can estimate load and a sensible availability target. I'd write every assumption on one page, like 'up to twenty thousand orders a day, with peaks at month end', and ask them to confirm or correct it. The design then leans on parts that can grow without a redesign, like managed databases and services that scale out, and avoids expensive choices we can't justify yet. I'd also agree review points, such as after the first month of live traffic, and put monitoring in from day one so the next decision uses real numbers."

"At the edge I'd put DNS and a CDN for static content, a web application firewall, and then a load balancer or API gateway. The application tier is stateless services running in at least two availability zones, so any instance can die without users noticing. Anything slow, like emails or reports, goes through a queue to background workers. For data, a managed relational database with a standby in another zone and point-in-time backups, a cache for hot reads, and object storage for files. Across all of it: single sign-on for staff, a customer identity service, secrets in a vault, logs, metrics and traces, and automated deployments. The usual weak points are the database as the hardest thing to scale, session state sneaking onto servers, third-party calls with no timeouts, and backups nobody has ever restored."

"First I'd check whether the pain really comes from the architecture. If releases block each other because of one shared release train and a long manual test cycle, better pipelines and feature flags might fix most of it without splitting anything. But if six teams keep editing the same code and one team's change breaks another's, that's a real case, and with forty engineers independent releases can pay for their cost. I'd look at which parts of the code change together and where the business domains sit, and draw boundaries that match team ownership. Before splitting, I'd check the basics: automated deployment, monitoring and tracing, and teams ready to run what they own in production. Then I'd recommend pulling out one or two services where the boundary is clearest, each owning its own data, and keeping the rest as a modular monolith. I'd write the criteria into a decision record so later splits are judged the same way."

"I start from what performance and downtime are worth to the business. If an hour of checkout downtime loses a lot of revenue, paying for a second region might be cheap insurance. If it's an internal report used once a week, it isn't. Then I usually lay out three options: a single region with good backups, a design spread across zones with automatic failover, and an active setup across two regions. For each I show the rough monthly run cost, the build effort, the expected downtime in a bad event, and the extra complexity for the team. I give my recommendation and why, and I'm explicit about what we're accepting. Then I add triggers, like 'if traffic doubles or we sign contracts with uptime penalties, we move to option three', so the decision doesn't get stuck."

"On a booking platform, I chose a document database for everything because the early data looked flexible and the team knew it well. Within a year, finance needed reports joining bookings, payments and refunds, and those queries were slow and painful to write. We were also enforcing rules in application code that a relational schema would have given us for free. The signals were a growing pile of reporting tickets and a couple of data inconsistencies. I said plainly in the architecture review that my choice no longer fitted our needs. We kept the document store for booking content, moved financial records into a relational database, and fed a reporting store from events. What I changed afterwards was asking harder questions up front about reporting and consistency, and writing down the assumptions behind a decision so we can check them later."

"My first question is whether this capability sets the business apart. Customer support tooling usually doesn't, which points towards buying. Then I'd gather the must-have requirements with the support team and score two or three products against them, including the integrations we'd need, like the CRM, single sign-on and order data. I'd compare total cost over a few years, not just the first: licences, integration work and admin effort for buying, versus the build, hosting and a team to keep it going for building. People always underestimate that last part. I'd also check the vendor's security and compliance, where the data is stored, and whether we can get all our data out if we leave. If a product covers most needs through configuration rather than custom code, I'd recommend buying and building only the thin layer that's specific to us."

"I use a synchronous call when the caller genuinely can't continue without the answer. Checking a customer's credit limit before accepting an order is a good example. The cost is coupling: if the other system is slow or down, so am I, so I need timeouts, limited retries with backoff and a sensible fallback. I use messages or events when the work can happen a little later, or when several systems need to react to the same fact. 'Order placed' might trigger billing, shipping and a confirmation email, and none of those should block the customer. Async gives me buffering and looser coupling, but I pay elsewhere: messages can arrive twice or out of order, so consumers must be idempotent, and I need to watch queue lag and dead letters. Many real flows mix both: a quick synchronous acceptance, then asynchronous processing."

"An API gateway gives clients one front door. It routes requests to the right backend, usually terminates TLS, checks tokens or API keys, applies rate limits and quotas, and gives consistent logging and metrics for every call. For partner APIs it often comes with key management and a developer portal, which saves a lot of work. What I'm careful about is business logic creeping in. It's tempting to add a transformation here and a rule there, and soon the gateway holds logic nobody can test properly, owned by a central team that becomes a bottleneck. So I keep it to cross-cutting concerns. If a client needs data from several services combined, I'd rather use a small backend-for-frontend service owned by that client's team. And because every request passes through it, the gateway itself has to be highly available."

"I'd start by mapping every integration: what triggers it, what data it moves, how often, and who owns it. The key question is which system is the source of truth for customers, products, orders and invoices, because point-to-point tangles usually come from nobody owning that. For the target, each system exposes APIs for things others need to ask or command, and publishes events to a shared event bus for things that happened, like 'invoice paid'. Other systems subscribe instead of being called directly, so adding a new consumer doesn't touch the publisher. I'd agree event contracts with clear versioning rules. Then I'd move gradually, starting with the flows that break most, running old and new in parallel and comparing results before switching off each script. Finally, the platform needs monitoring, dead-letter queues and replay, or the new setup becomes the next mystery."

"For me it means security decisions shape the diagram rather than get bolted on afterwards. Early on I run a light threat model: what data and actions are valuable, where the entry points and trust boundaries are, and who might attack them. Then some controls go in by default. Every user and service has an identity, with single sign-on for staff and short-lived credentials for services, and each gets only the access it needs. Data is encrypted in transit and at rest, secrets live in a vault, and the network is split so the database isn't reachable from the internet. We only collect and keep the data we need, which cuts both risk and compliance work. Security logs go somewhere central with alerts, and the pipeline scans dependencies and infrastructure code. And I bring the security team in at the design stage, not at go-live."

"Three weeks before launching a customer analytics feature, our data protection team pointed out that we were copying full customer records, contact details included, into the analytics store, which went beyond what our privacy notice allowed. Rebuilding the pipeline in time wasn't realistic. We weighed three options: delay, launch with a narrower dataset, or pseudonymise the data on the way in. We chose to replace direct identifiers with tokens at ingestion, keep the mapping in a separate locked-down service, and drop fields we didn't need. We launched about a week late, with the data protection lead's sign-off. Afterwards I added a data classification step and a short privacy checklist to our design template, and invited security and privacy people to the first design review instead of the last. We didn't have a late surprise like that again."

"I'd sit down with the team and go through the list item by item, because 'skip security' usually hides a mix. Some things are non-negotiable, like authentication, encryption of personal data, and fixing any high-severity findings. I'd hold firm on those and help them find the time, maybe by cutting a feature from the launch scope instead. Other items, like a full failover test or hardening an internal admin screen, might safely follow a few weeks after launch if we add compensating controls, such as a tested backup restore and limiting that admin screen to the internal network. For anything deferred, I'd write the risk down plainly and have the business owner accept it, with a date to close it. My job isn't to block the launch or to quietly agree. It's to make sure the person accountable knows exactly what risk they're taking."

"I'd start by understanding what the old system actually does, including the undocumented rules people rely on. I'd map capabilities, integrations and data, and find the slices that can move on their own. Then I'd use a strangler approach: put a routing layer, often a gateway or a facade, in front of the legacy system, and move one capability at a time behind it. The first slice should be low risk but teach us a lot, like a read-only customer lookup. Data is the hard part, so for each slice I decide which side is the source of truth, how we sync in the meantime, and how we reconcile to prove nothing was lost. Every step has a rollback, usually just pointing the route back. And I plan the retirement too, because a migration that never switches off the old system just doubles the running cost."

"I led the move of our customer portal from our own servers to a managed cloud platform. We moved static content and the read-only account pages first, because they were low risk and would test our networking, monitoring and deployment pipeline. The surprise came with the login flow. The old system relied on a nightly file from HR for staff accounts, which nobody had documented, and after the move some support staff couldn't log in. We switched that route back within the hour, because every step had a path back to the old system. Then we added the file feed to the new setup and ran both in parallel for a week before switching again. The lesson I took was to watch real traffic and scheduled jobs on the old system before moving anything, not just read its documentation."

"An architecture decision record is a short document, often a page or less, that captures one significant decision and why we made it. The usual parts are a title, a status, the context at the time, the decision, and its consequences, good and bad. I also add the date and the options we looked at. For example: we use a message queue between orders and billing, and we accept that the invoice screen may lag a few seconds behind. To keep them useful, I keep them short, store them in the repository next to the code so they're versioned and easy to find, and link them from design reviews and pull requests. When a decision changes, we don't rewrite the old record, we add a new one that supersedes it. That history is the real value: a year later, someone can see why we chose something, not just what we chose."

"I'd start by listening. I'd talk to the people who've been there longest and look at the code, old tickets and incident reports to rebuild why things are the way they are. Then I'd write a short overview of the current architecture, a few diagrams at different levels, and backfill decision records for the five or six decisions that keep coming up, marked honestly where the reason is a best guess. For each debate that keeps reopening, I'd run one proper session, agree a decision, and record it, including what new facts would make us revisit it. That part matters, because the aim isn't to freeze decisions, it's to stop rehashing them without new information. Going forward, I'd make a decision record part of any significant change, kept next to the code and linked from reviews."

"In my last role I needed funding to replace our nightly file-based integrations with an event-driven setup. I knew the leadership team didn't care about queues, so I opened with the problem in their terms: orders placed after the evening batch waited until the next day, and failures were often found by customers before we found them. Then I showed three options with cost, time to deliver and risk, and recommended the middle one. The finance director challenged the running cost, fairly. I'd prepared a breakdown showing we'd retire two licensed tools, which covered most of it. The CEO asked what happens if it fails halfway through. I explained we'd move one flow at a time with the old batch kept as a fallback. They approved it in the meeting, with a review after the first two flows."

"We'd agreed that services would publish business events through a shared event platform. Two teams pushed back hard. They said it would slow them down and they'd rather call each other's APIs directly. Instead of escalating, I spent time with each team. Part of their complaint was fair: there was no local testing setup, and publishing an event needed a lot of boilerplate. So I worked with the platform team to add a small library and a local emulator, and I paired with one team to move their first flow. When they saw it took a day rather than a week, and that they stopped getting paged when a downstream service was slow, they became the ones explaining it to others. I also changed the standard: direct API calls were fine for simple queries, events only for things that had happened. That compromise made it stick."

"First I'd find out why they want it. Maybe a new CTO wants portability, or they've been burned by lock-in before, or someone wants the skills on the team. Those are different problems. Then I'd compare their option with, say, a managed container service, measured against their own goals: portability, cost, time to launch, and what their two-person team would have to run on nights and weekends, like upgrades, patching and security fixes. I'd make it concrete, not a lecture. Often the real goal, like portability, can be met more cheaply with standard containers on a managed service. If they still want their option, it's their system and their call, so I'd write down the risks and the staffing it needs, and make the design as safe as I can. If it were genuinely unsafe, I'd raise it with my manager and the account lead."

"I'd open with the ask and the outcome in one sentence: we need this budget over two quarters to replace the order platform's database, which removes our biggest outage risk and lowers licence costs from next year. Then the cost of doing nothing, in money and risk terms: how often it's failed, roughly what each outage cost us in lost orders, and that vendor support ends next year. Then the options, briefly: do nothing, a cheap patch, and my recommendation, with why the patch only delays the bill. I'd finish with how we'll track it, like milestones every six weeks and a point where we can stop if it isn't working. No diagrams, and no product names unless asked. And I'd leave a one-page summary, because the CFO will probably need to defend it to others."

"Once it's decided, I back it properly, in public and in private. The team needs to know I'm not hoping it fails. The concerns I raised don't disappear, so I turn them into help. If I was worried about performance, I'd help them set up load tests early and agree together which numbers would tell us there's a problem. That turns my objection into a shared check instead of a grudge. I also stay open to being wrong. The team often knows things about their constraints that I didn't weigh enough, and more than once their approach has worked better than I expected. If the warning signs do show up, I raise them early and factually, with the team first, not in a meeting with their manager. People remember how you behaved when you lost an argument far more than whether you were right."

"We were competing for a logistics customer who wanted to connect their carriers to our platform. The other vendors gave polished product demos. I asked the account manager for a working session with their integration team instead, and we learned their real worry was a handful of old carriers who could only send files, not call APIs. So I built a small proof of concept that took one of their real carrier files, mapped it to our events and showed shipment updates on our dashboard. I also wrote a short architecture note showing file-based and API carriers side by side. Their IT lead said it was the first time a vendor had dealt with their actual mess. We won, and that note became the starting point for delivery, so there was no gap between what we sold and what we built."

"I'd say plainly that it doesn't do that today, because a customer finding out after signing costs far more than losing a point now. But I wouldn't stop there. I'd ask what they're trying to achieve, because a feature request is often a means to an end. If they need approval workflows, for example, maybe our product plus their existing workflow tool through our API covers it, and I could sketch that on the call. If it's on our roadmap, I'd only mention it if it's confirmed, and I'd say I'll check timing with the product team rather than guess a date. After the call I'd talk to the account executive privately so we're aligned for next time, and log the request with product so they know a real customer asked for it."